electricsheepeurope/europe-ilo-eip-neet-sex-age-nb-youth-not-in-employment-education-or-training-neet

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Youth not in employment, education or training (NEET) by sex and age (thousands) | Europe (ILOSTAT)" --- # Youth not in employment, education or training (NEET) by sex and age (thousands) | Europe (ILOSTAT) 🇪🇺 **9,773 observations** · **39 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **9,773 observations** of `Other measures of labour underutilization` data across **39 Europe countries**, spanning **1983–2025**, covering **1 distinct indicators**. ## About the source **ILOSTAT** is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation. - **Source:** [ILOSTAT](https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_AGE_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_NEET_SEX_AGE_NB` and filtered to Europe ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the `source.label` column for traceability. ## Geographic coverage 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `BEL` | 378 | 1983 | 2024 | | `FRA` | 378 | 1983 | 2024 | | `DNK` | 378 | 1983 | 2024 | | `LUX` | 378 | 1983 | 2024 | | `ITA` | 378 | 1983 | 2024 | | `NLD` | 360 | 1983 | 2024 | | `ESP` | 360 | 1986 | 2025 | | `PRT` | 360 | 1986 | 2025 | | `GRC` | 351 | 1987 | 2025 | | `GBR` | 351 | 1983 | 2025 | | `DEU` | 342 | 1983 | 2024 | | `IRL` | 342 | 1983 | 2024 | | `CHE` | 315 | 1991 | 2025 | | `AUT` | 261 | 1995 | 2025 | | `HUN` | 261 | 1996 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EIP_NEET_SEX_AGE_NB` — Youth not in employment, education or training (NEET) by sex and age (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `ALB` | | `ref_area.label` | `string` | Country name in English | `Albania` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_NEET_SEX_AGE_NB` | | `indicator.label` | `string` | Indicator name in English | `Youth not in employment, education or…` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `AGE_5YRBANDS_YGE15` | | `classif1.label` | `string` | — | `Age (5-year bands): 15+` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `59.496` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## Data quality & caveats - Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here. - When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used. - Disaggregation columns (`sex`, `classif1`, `classif2`) are non-null only when the indicator publishes that breakdown. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepeurope/europe-ilo-eip-neet-sex-age-nb-youth-not-in-employment-education-or-training-neet") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python germany = df[df["ref_area"] == "DEU"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EIP_NEET_SEX_AGE_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_NEET_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_NEET_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eip_neet_sex_age_nb_youth_not_in_employment_education_or_training_neet_2025, title = {Youth not in employment, education or training (NEET) by sex and age (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eip-neet-sex-age-nb-youth-not-in-employment-education-or-training-neet}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging. ## About Electric Sheep Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepeurope](https://huggingface.co/electricsheepeurope) --- _Provenance: ingested 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_AGE_NB_This dataset is a tabular dataset focusing on the number of youth not in employment, education or training (NEET) by sex and age (in thousands) across 39 European countries. It contains 9,773 observations spanning from 1983 to 2025, covering one core indicator: EIP_NEET_SEX_AGE_NB, which measures youth not engaged in employment, education, or training. The data is sourced from the International Labour Organization (ILO)s ILOSTAT statistical database, retrieved via REST API and processed to include only European countries. The dataset includes columns such as country codes, source information, indicator codes, sex disaggregation (total, male, female), age groups, observation year, observed values, and status flags, making it suitable for tasks like tabular classification, regression, and time-series forecasting. The data is provided at an annual frequency and has been harmonized by the ILO to ensure consistency with International Conference of Labour Statisticians (ICLS) definitions.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的ILOSTAT核心统计数据库，聚焦于欧洲地区青年未就业、未受教育或培训（NEET）的量化指标。构建过程严格遵循国际劳工统计学家会议（ICLS）定义，通过ILOSTAT REST API直接提取原始调查微观数据，并依据ISO3国家代码精确筛选至39个欧洲国家。数据整合了来自劳动力调查、家庭收入调查及行政记录等多源信息，经ILO统计部门统一协调与标准化处理，最终形成包含9,773条观测值、时间跨度自1983年至2025年的年度面板数据。每个观测值均附有来源标签以保障可追溯性，确保了数据在跨国比较中的一致性与权威性。

使用方法

该数据集以HuggingFace Datasets库的标准格式发布，极大简化了加载流程。用户仅需通过Python调用`load_dataset("electricsheepeurope/europe-ilo-eip-neet-sex-age-nb-youth-not-in-employment-education-or-training-neet")`即可获取并转换为Pandas DataFrame进行后续分析。针对特定国家的聚焦研究，可通过`ref_area`字段（如'DEU'代表德国）进行快速筛选。对于单指标的时间序列分析，可依据`indicator`和`time`字段排序后绘制趋势图。此外，利用`pivot_table`功能可轻松构建国家×年份的面板数据矩阵，便于开展跨国的比较计量经济学建模。

背景与挑战

背景概述

在全球青年就业形势日益严峻的背景下，青年既不就业也不接受教育或培训（NEET）的群体规模已成为衡量劳动力市场健康与社会包容性的关键指标。国际劳工组织（ILO）作为全球劳动统计的权威机构，其ILOSTAT数据库系统收录了涵盖200多个经济体的劳动指标，为政策制定与学术研究提供了坚实基础。本数据集由Electric Sheep Europe于2025年重新整理发布，聚焦欧洲39个国家的NEET指标，时间跨度从1983年至2025年，包含9,773条观测记录，按性别与年龄维度进行细致分类。该数据集直接调用ILOSTAT的REST API获取官方数据，并严格遵循国际劳工统计学家会议（ICLS）定义进行数据协调，成为研究欧洲青年劳动力市场演化、比较各国NEET趋势以及评估相关社会政策成效的重要基准资源，在劳动经济学、教育政策与社会学领域具有广泛影响力。

当前挑战

该数据集所应对的核心挑战在于精准量化欧洲青年劳动力市场的非充分就业形态，尤其是NEET群体这一复杂社会现象，其涉及就业、教育与培训的多维度交叉，传统统计指标难以全面刻画。数据构建过程中面临多重困难：首先，39个国家近半世纪的数据采集依赖不同年代的调查工具与统计口径，各国劳动力调查、家庭收入调查等行政记录在频率、分类标准与质量上参差不齐，ILO虽进行了统一协调，但数据源字段中的连续性中断与方法学修订（如标为'Break in series'的观测）仍提示历史可比性挑战；其次，性别与年龄的详细分解虽充实了分析层次，但部分分类维度存在缺失，且年度频率数据无法捕捉月度或季度的短期波动，限制了时间序列建模的精度；此外，ILO对同一国家同年份的多源数据仅选取'最佳来源'，可能引入选择偏差，需要用户结合源标签追溯原始调查背景以确保分析结论的稳健性。

常用场景

经典使用场景

在欧洲青年劳动力市场研究领域，该数据集的核心价值在于提供了跨越近半个世纪（1983年至2025年）、覆盖39个欧洲国家的青年NEET（未就业、未受教育或未接受培训）人口统计信息。研究人员能够利用这些按性别和年龄分层的数据，构建面板数据模型，分析不同国家青年NEET比例的长期演变趋势与周期性波动。这一数据集为比较政治经济学和劳动社会学领域探讨福利制度、教育政策与劳动力市场结构对青年边缘化状态的影响，提供了坚实且标准化的量化基础。

解决学术问题

该数据集有效回应了欧洲青年研究中的若干关键学术议题。它使得学者能够精确衡量不同社会经济群体（如按性别、年龄细分）的NEET风险差异，从而深入解析结构性不平等与代际传递问题。此外，通过时间维度的丰富覆盖，研究者可以评估重大社会事件（如2008年金融危机、新冠疫情）或特定政策干预对青年就业与教育参与的异质性冲击效应，为实证因果推断提供了宝贵的纵向数据支撑，推动了劳动经济学与社会政策评估领域的发展。

实际应用

在实际应用层面，该数据集是国际组织、各国政府和民间智库进行青年政策制定与效果监测的核心工具。例如，欧盟委员会和欧洲各国劳动部门可据此追踪《欧洲青年保障计划》的实施进展，识别高NEET风险地区与人群，从而实现精准资源配置。此外，社会服务机构和非营利组织能利用这些数据开展区域性需求评估，设计针对性的职业培训与心理支持项目，助力青年重返就业或教育轨道，有效降低社会排斥风险。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲青年既不就业也不接受教育或培训（NEET）的时序人口统计，为劳动经济学与社会政策研究提供了精细化的性别与年龄分层观测窗口。当前前沿研究方向集中于利用此类长跨度面板数据（1983–2025年）与先进的机器学习模型，如时间序列Transformer或因果森林，来量化欧洲各国NEET率的长期趋势、结构性断点以及社会经济冲击（如疫情、数字化转型）对青年劳动参与度的异质性影响。同时，该数据与ILO劳动利用不足指标体系的衔接，为跨国比较青年脆弱就业形态与SDG体面劳动目标的实现进度提供了标准化的数据基础，推动了从描述性统计向预测性政策模拟的范式转变。